Object tracking in an important tool used in many imaging software applications. One issue that commonly arises in tracking the motion of an object is distinguishing the movement of an object from a moving background. An example of a scenario is recognizing movement of human features such as head movements, facial features, hand movements or other body movements. In imaging the target image, it becomes clear that the background scenery is also moving in the image (e.g., trees, vehicles, and people). This makes it difficult to track the object, for example, a facial feature. The imaging software must be able to adequately distinguish between the target (i.e., the particular facial feature) and the other image data.
Object tracking is also important in medical imaging applications such as echocardiography. Accurate analysis of the myocardial wall motion in cardiac ultrasound images is crucial for the evaluation of the heart function. One of the difficulties in tracking myocardial wall function is to compensate for additional motion introduced by breathing, movement of the body or the ultrasound probe. The effects of these motions can be reduced either during image acquisition by preventing patient motion (breath-holding, careful probe placement) or in the post-processing stage through image-based correction techniques. However, true cardiac motion cannot be obtained without compensating the external motion.
Cardiac motion can be broken down into local motion and global motion. Local motion refers to the internal movements of the heart. In other words, it is the movement of the myocardium during systole and diastole. Global motion is the external movements other than the local motion. As indicated above, it can originate from many sources such as small body motion or breath of the patient or the movement of the imaging device or hand of the radiologist while imaging.
If no compensation is made for global motion, misdiagnosis can occur. For example, without compensation a patient may be diagnosed with ischemia at the right side of the left ventricle because the contraction of the right segments looks much less than other segments. This could happen because global motion to the right would offset the motion of the right wall and amplify the motion of the left wall. After compensation, the contractions in each segment are similar, which indicates normal movement of the heart. Likewise, a patient may be diagnosed with a normal heart but have an ischemia. An ischemic left ventricle may be regarded as normal if global motion exists. In many instances, the existence of global motion may affect the accuracy of diagnosis, regardless of whether the diagnosis is made by a doctor or an intelligent machine.
Previous methods for compensating for global motion include long axis and principal axis methods. Principal axis is defined as the line in space that has the weighted least-squared distances from the centroids of all the given cross sections. The left ventricle centroid at the end of systole has been used with the principal axis to determine the translation and rotation factors. The image data and principal axis are obtained for two continuous image frames. The centroid is decided by a certain predefined frame (e.g., end-of-systole). Two frames are then overlapped to decide the translation by the movement of the centroid. After translation, the rotation angle can be decided and compensation can be achieved by the translation and rotation factors. The principal axis is not widely used for global motion compensation because it does not adequately identify an ischemic region of a heart or similar analysis. The principal axis is affected by abnormal regions movement and cannot be used to determine true motion.
Another method presumes that the shape of a normal heart remains almost the same during systole. In the case of an ischemic heart, the motion is significantly modified in and around the infarcted zone resulting in hypokinesia, akinisia, or dyskiniesia. The global shape and curvedness distribution is observed during the heart cycle as well as the local shape for a number of data points (e.g., apex, anterior and pit). The global and local shape and curvedness distribution of each data point are combined together to compare with normal heart data and then the abnormal regions can be determined. In the local region tracking, the principal axes are used to compensate the global motion. The problem with this method in identifying the abnormal region lies with the original assumption that was made. As indicated above, the assumption was made that the shape of a normal heart remains the same during systole. However, the heart itself has local rotation or torsion and the shape of a normal heart can be very different during systole in many instances. There is a need to adequately compensate for global motion in order to improve the accuracy of medical diagnoses.